Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Censoring Survival Data

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...

Chemical Formulas

Chemical Formulas

A chemical formula presents information about the proportions of atoms constituting a particular chemical compound or molecule, mainly using symbols of elements and numbers. At times other symbols, such as dashes, parentheses, brackets, commas, plus, and minus signs, are also used. A chemical formula can be one of three types – molecular, empirical, and structural.

Types of Chemical Bonds

Types of Chemical Bonds

Chemical bonding theories were pioneered by American chemist Gilbert N. Lewis. He developed a model called the Lewis model to explain the type and formation of different bonds. Chemical bonding is central to chemistry; it explains how atoms or ions bond together to form molecules. It explains why some bonds are strong and others are weak, or why one carbon bonds with two oxygens and not three; why water is H2O and not H4O.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Chemical Equations

Chemical Equations

Chemical equations represent the identities and relative quantities of substances involved in a chemical reaction. The substances undergoing reaction are called reactants, and their formulas are placed on the left side of the equation. The substances generated by the reaction are called products, and their formulas are placed on the right side of the equation. Plus signs (+) separate individual reactant and product formulas, and an arrow (→) separates the reactant and product (left and right)...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same authorSame journal

High-dimensional multivariate geostatistics: A Bayesian matrix-normal approach.

Environmetrics·2026

Same author

Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.

Allergy·2026

Same authorSame journal

Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking.

Environmetrics·2026

Same author

Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking.

Bayesian analysis·2026

Same author

Developing PPE management tools for use in a pandemic.

Journal of occupational and environmental hygiene·2026

Same author

Invisible Threats, Concrete Actions: Analyzing and Enhancing OSHA's Infectious Disease Framework.

Health security·2026

Search research articles

Related Experiment Video

Updated: Feb 2, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Multivariate left-censored Bayesian model for predicting exposure using multiple chemical predictors.

Caroline Groth¹, Sudipto Banerjee², Gurumurthy Ramachandran³

¹Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611.

|November 24, 2018

Summary

This summary is machine-generated.

This study introduces a new statistical method to estimate chemical exposure from mixtures when data is missing or censored. The framework provides unbiased exposure assessments, crucial for environmental health professionals.

Keywords:

Correlations Deepwater Horizon Oil Spill chemical mixtures exposure assessment

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Related Experiment Videos

Last Updated: Feb 2, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Area of Science:

Environmental Health
Occupational Exposure
Chemical Mixtures Analysis

Background:

Assessing exposure to airborne chemical mixtures is vital for environmental health.
Exposure data is often incomplete due to unmeasured chemicals or censored data below detection limits.
Existing methods like bivariate analyses are limited when chemical correlations are low.

Purpose of the Study:

To develop a multivariate statistical framework for estimating chemical exposure from mixtures.
To account for censored exposure measurements in all chemicals within a mixture.
To provide unbiased exposure estimates for environmental health assessments.

Main Methods:

Developed a multivariate framework utilizing chemical relationships within mixtures.
Incorporated methods to handle censored data across multiple chemical measurements.
Assessed model performance against simpler models at various censoring levels and evaluated 95% coverage.

Main Results:

The proposed multivariate framework yields unbiased exposure estimates.
The model effectively handles censored data, improving accuracy in exposure assessment.
Demonstrated application in assessing vapor exposure to crude oil chemicals during the Deepwater Horizon response.

Conclusions:

The multivariate framework offers a robust solution for exposure assessment with incomplete chemical mixture data.
This approach enhances the ability of environmental health professionals to evaluate risks from complex chemical exposures.
The study provides a valuable tool for analyzing environmental contamination scenarios, particularly after events like oil spills.